\section{Introduction}
Program authorship plays an important role in code plagiarism detection and security. 
In this paper, we propose an innovative authorship attribution technique to identify code authors based on binary code. 
It avoids the embarrassment that when analyzing malware, we usually do not have source code. 
To achieve this goal, we firstly extract features from binary code, then we do feature selection because the feature space is huge. 
Finally, We use linear support vector machine for classification. 
We experiment on Dyninst [1] project repository, from which we extract 11782 functions with 44 authors as our data set and our model can achieve 62\% accuracy.

This paper is organized as follows. 
In section 2, we briefly discuss the related work.
In section 3, we introduce the binary code features in details. 
In section 4, we propose our machine learning model. 
In section 5, we describe our experiments to verify our model. 
In section 6, we conclude the paper.
